INVESTIGATION ON THE MORPHOLOGICAL AND THERMAL PROPERTIES OF POLYLACTIC ACID/POLYCAPROLACTONE LOADED WITH HALLOYSITE NANOTUBES AND BASALT FIBER

This paper is focused on the analysis of the morphological and thermal properties of the biomedical composites, polylactic acid (PLA) and polycaprolactone (PCL) matrix, reinforced with basalt fibers (BFs) and using halloysite nanotubes (HNT) as filler material. Four different composites, viz. PPHB 1, PPHB 2, PPHB 3 and PPHB 4, are obtained by varying the weight fractions of these materials using twin-screw extrusion followed by injection molding. The morphological characterization is performed on these composites using scanning electron microscopy (SEM) and Fourier transform infrared (FTIR) spectroscopy. SEM reveals homogenous and strong bonding between the matrix, reinforcement and filler. The BF are well embedded in the matrix with a random orientation. No formation of voids and cracks is observed. The functional groups present and the types of vibration experienced by the chemical bonds were observed in the FTIR spectra. The composites are subjected to thermal testing such as differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA). The PPHB 2, which contains 80% PLA, 10% BF, 7% PCL and 3% HNT, has the highest degree of crystallinity, as revealed by DSC, and exhibits the most optimum thermal degradation characteristics as indicated by TGA.

New insights in polydopamine formation via surface adsorption

Polydopamine is a biomimetic self-adherent polymer, which can be easily deposited on a wide variety of materials. Despite the rapidly increasing interest in polydopamine-based coatings, the polymerization mechanism and the key intermediate species formed during the deposition process are still controversial. Herein, we report a systematic investigation of polydopamine formation on halloysite nanotubes; the negative charge and high surface area of halloysite nanotubes favour the capture of intermediates that are involved in polydopamine formation and decelerate the kinetics of the process, to unravel the various polymerization steps. Data from X-ray photoelectron and solid-state nuclear magnetic resonance spectroscopies demonstrate that in the initial stage of polydopamine deposition, oxidative coupling reaction of the dopaminechrome molecules is the main reaction pathway that leads to formation of polycatecholamine oligomers as an intermediate and the post cyclization of the linear oligomers occurs subsequently. Furthermore, Tris molecules are incorporated into the initially formed oligomers.

Effect of Halloysite Nanotubes on Physico-Mechanical Properties of Silk/Basalt Fabric Reinforced Epoxy Composites

Natural fiber reinforced polymer composites have become more attractive due to their high specific strength, light weight and environmental concern. However, some limitations such as low modulus and poor moisture resistance were reported. This paper presents the role of halloysite nanotubes (HNTs) on physico-mechanical properties of bidirectional silk and basalt fiber reinforced epoxy (SF-BF/Ep) hybrid composites. Vacuum bagging and ultra-sonication method were used for the fabrication of hybrid composite slabs. The effect of HNT loadings (1.5, 3 and 4.5 wt. %) on physico-mechanical characteristics like density, hardness, flexural and impact properties of SF-BF/Ep composites were determined according to ASTM standards. Experimental results revealed that the incorporation of HNTs improves the mechanical properties. The impact strength of SF-BF/Ep is predominant at 3 wt. % HNT loading where the impact strength surges to 568.67 J/m, which may render HNT filled SF-BF/Ep desirable for various toughness-critical structural applications. The test results demonstrated that SF-BF/Ep-3HNT coded composites exhibited improved mechanical properties among the all composites.

Experimental study and computational intelligence on dynamic viscosity and thermal conductivity of HNTs based nanolubricant

Purpose This paper aims to evaluate the dynamic viscosity and thermal conductivity of halloysite nanotubes (HNTs) suspended in SAE 5W40 using machine learning methods (MLMs). Design/methodology/approach A two-step method with surfactant was selected to prepare nanolubricants in concentrations of 0.025, 0.05, 0.1 and 0.5 wt%. Thermal conductivity and dynamic viscosity of nanofluids were ascertained over the temperature range of 25–70 °C, with an increment step of 5 °C, using a KD2-Pro analyser device and a digital viscometer MRC VIS-8. Additionally, four different MLMs, including Gaussian process regression (GPR), artificial neural network (ANN), support vector machine (SVM) and decision tree (DT), were used for predicting dynamic viscosity and thermal conductivity by using nanoparticle concentration and temperature as input parameters. Findings According to the achieved results, the dynamic viscosity and thermal conductivity of nanolubricants mostly increased with the rise of nanoparticle concentration in the base oil. All the proposed models, especially GPR with root mean square error mean values of 0.0047 for dynamic viscosity and 0.0016 for thermal conductivity, basically showed superior ability and stability to estimate the viscosity and thermal conductivity of nanolubricants. Practical implications The results of this paper could contribute to optimising the cost and time required for modelling the thermophysical properties of lubricants. Originality/value To the best of the author’s knowledge, in this available literature, there is no paper dealing with experimental study and prediction of dynamic viscosity and thermal conductivity of HNTs-based nanolubricant using GPR, ANN, SVM and DT.